Octree-Retention Fusion: A High-Performance Context Model for Point Cloud Geometry Compression
Author:
Affiliation:
1. Ningbo Key Lab of DSP, Zhejiang Wanli University & Ocean University of China, Ningbo, China
2. Ningbo Key Lab of DSP, Zhejiang Wanli University, Ningbo, China
Funder
Yongjiang Sci-Tech Innovation 2035
Ningbo Municipal Major Project of Science and Technology Innovation 2025
Zhejiang Provincial Natural Science Foundation of China
National Natural Science Foundation of China,
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3652583.3657620
Reference25 articles.
1. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
2. Muscle: Multi sweep compression of lidar using deep entropy models;Biswas Sourav;Advances in Neural Information Processing Systems,2020
3. OctFormer: Efficient Octree-Based Transformer for Point Cloud Compression with Local Enhancement
4. Eugene d'Eon, Bob Harrison, Taos Myers, and Philip A Chou. 2017. 8i voxelized full bodies-a voxelized point cloud dataset. ISO/IEC JTC1/SC29 Joint WG11/WG1 (MPEG/JPEG) input document WG11M40059/WG1M74006, Vol. 7, 8 (2017), 11.
5. Real-Time Spatio-Temporal LiDAR Point Cloud Compression
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